| Presentation | 2011/6/16 Model-based identification of synaptic connectivity from multi-neuronal spike train data JUNICHIRO YOSHIMOTO, KENJI DOYA, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The paper presents a method to identify synaptic connectivity from multineuronal spike train data. In this method, a stochastic spiking neural network model is derived on the basis of generalized leaky integrate-and-fire neurons connected with multi-exponential post-synaptic current function. The model parameters are fitted based on maximum likelihood estimation and a sparse Bayesian framework. Then, the model is employed to quantify how much a spike of the pre-neuron changes the potential of the post-neuron. Based on the quantities, the synaptic connectivity between two neurons are identified. The basic performance was demonstrated by applying the method to two synthetic benchmarks. The results showed that the method was able to identify the synaptic connectivity in the benchmarks with a high precision. |
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| Paper # | Vol.2011-BIO-25 No.4 |
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| Committee | NC |
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| Conference Date | 2011/6/16(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Registration To | Neurocomputing (NC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Model-based identification of synaptic connectivity from multi-neuronal spike train data |
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| 1st Author's Name | JUNICHIRO YOSHIMOTO |
| 1st Author's Affiliation | Neural Computation Unit, Okinawa Institute of Science and Technology:Graduate School of Information Science, Nara Institute of Science and Technology() |
| 2nd Author's Name | KENJI DOYA |
| 2nd Author's Affiliation | Neural Computation Unit, Okinawa Institute of Science and Technology:Graduate School of Information Science, Nara Institute of Science and Technology |
| Date | 2011/6/16 |
| Paper # | Vol.2011-BIO-25 No.4 |
| Volume (vol) | vol.111 |
| Number (no) | 96 |
| Page | pp.pp.- |
| #Pages | 6 |
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